Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations11925
Missing cells78110
Missing cells (%)18.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory280.0 B

Variable types

Numeric8
Text15
DateTime8
Categorical3
Unsupported1

Alerts

late has constant value "1.0"Constant
idle_reclaim_duration has constant value "0d0h0m0s"Constant
course_id is highly overall correlated with task_idHigh correlation
effective_reclaim_duration is highly overall correlated with reclaim_countHigh correlation
reclaim_count is highly overall correlated with effective_reclaim_durationHigh correlation
task_id is highly overall correlated with course_idHigh correlation
turned_in is highly overall correlated with turnin_count and 1 other fieldsHigh correlation
turnin_count is highly overall correlated with turned_inHigh correlation
user_id is highly overall correlated with turned_inHigh correlation
late has 8396 (70.4%) missing valuesMissing
grade has 4489 (37.6%) missing valuesMissing
first_turnin_time has 3552 (29.8%) missing valuesMissing
first_reclaim_time has 3552 (29.8%) missing valuesMissing
Unnamed: 14 has 11343 (95.1%) missing valuesMissing
first_reclaim_to_last_turnin_duration has 11343 (95.1%) missing valuesMissing
mean_reclaim_duration has 11343 (95.1%) missing valuesMissing
mean_effective_reclaim_duration has 11343 (95.1%) missing valuesMissing
mean_time_between_reclaims has 11343 (95.1%) missing valuesMissing
grade is highly skewed (γ1 = 59.46834175)Skewed
effective_reclaim_duration is highly skewed (γ1 = 61.32975017)Skewed
time_usage_ratio is highly skewed (γ1 = 40.55310905)Skewed
id has unique valuesUnique
available_time is an unsupported type, check if it needs cleaning or further analysisUnsupported
grade has 1297 (10.9%) zerosZeros
effective_reclaim_duration has 11304 (94.8%) zerosZeros
time_usage_ratio has 3528 (29.6%) zerosZeros
turnin_count has 3478 (29.2%) zerosZeros
reclaim_count has 11269 (94.5%) zerosZeros

Reproduction

Analysis started2024-07-25 15:27:29.156499
Analysis finished2024-07-25 15:27:56.137734
Duration26.98 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

course_id
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4423848 × 1011
Minimum1.6417721 × 1011
Maximum6.706952 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2024-07-25T12:27:56.325788image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1.6417721 × 1011
5-th percentile1.6747936 × 1011
Q12.1935205 × 1011
median4.833184 × 1011
Q35.9370746 × 1011
95-th percentile6.5900911 × 1011
Maximum6.706952 × 1011
Range5.0651799 × 1011
Interquartile range (IQR)3.7435541 × 1011

Descriptive statistics

Standard deviation1.8197912 × 1011
Coefficient of variation (CV)0.40964285
Kurtosis-1.3633582
Mean4.4423848 × 1011
Median Absolute Deviation (MAD)1.1889857 × 1011
Skewness-0.52689978
Sum5.2975438 × 1015
Variance3.3116399 × 1022
MonotonicityNot monotonic
2024-07-25T12:27:56.656092image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5.978932978 × 10111227
 
10.3%
5.935032118 × 1011733
 
6.1%
5.933503592 × 1011729
 
6.1%
5.937074568 × 1011668
 
5.6%
1.67510976 × 1011636
 
5.3%
6.068979186 × 1011606
 
5.1%
1.674793649 × 1011604
 
5.1%
4.833183799 × 1011599
 
5.0%
5.936884532 × 1011576
 
4.8%
1.678169416 × 1011484
 
4.1%
Other values (26) 5063
42.5%
ValueCountFrequency (%)
1.641772113 × 1011332
2.8%
1.674793649 × 1011604
5.1%
1.67510976 × 1011636
5.3%
1.676773979 × 1011331
2.8%
1.678169416 × 1011484
4.1%
1.678194498 × 1011293
2.5%
2.193483035 × 1011104
 
0.9%
2.193493458 × 101193
 
0.8%
2.19352051 × 1011108
 
0.9%
2.193524508 × 101181
 
0.7%
ValueCountFrequency (%)
6.70695201 × 101133
 
0.3%
6.680943488 × 1011135
 
1.1%
6.679753076 × 1011152
 
1.3%
6.679617492 × 1011123
 
1.0%
6.666940792 × 1011116
 
1.0%
6.590091147 × 1011148
 
1.2%
6.068979186 × 1011606
5.1%
6.022169692 × 1011241
 
2.0%
5.978932978 × 10111227
10.3%
5.937074568 × 1011668
5.6%

task_id
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7074092 × 1011
Minimum1.4522545 × 1011
Maximum6.7530189 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2024-07-25T12:27:56.877140image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1.4522545 × 1011
5-th percentile1.6809261 × 1011
Q13.0608944 × 1011
median5.2986042 × 1011
Q36.0820569 × 1011
95-th percentile6.5596989 × 1011
Maximum6.7530189 × 1011
Range5.3007644 × 1011
Interquartile range (IQR)3.0211625 × 1011

Descriptive statistics

Standard deviation1.642354 × 1011
Coefficient of variation (CV)0.34888703
Kurtosis-0.99913319
Mean4.7074092 × 1011
Median Absolute Deviation (MAD)8.8709858 × 1010
Skewness-0.68466334
Sum5.6135855 × 1015
Variance2.6973267 × 1022
MonotonicityNot monotonic
2024-07-25T12:27:57.107315image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.630276753 × 101145
 
0.4%
4.200303881 × 101145
 
0.4%
6.56116321 × 101145
 
0.4%
6.559859579 × 101145
 
0.4%
6.564759179 × 101145
 
0.4%
2.25793863 × 101145
 
0.4%
4.630276755 × 101145
 
0.4%
3.656625323 × 101145
 
0.4%
4.542019847 × 101145
 
0.4%
4.369364688 × 101145
 
0.4%
Other values (457) 11475
96.2%
ValueCountFrequency (%)
1.4522545 × 101138
0.3%
1.461246818 × 101138
0.3%
1.464314759 × 101124
0.2%
1.464399844 × 101124
0.2%
1.470358579 × 101138
0.3%
1.495602804 × 101132
0.3%
1.504354437 × 101124
0.2%
1.577200926 × 101124
0.2%
1.577204327 × 101130
0.3%
1.577463769 × 101130
0.3%
ValueCountFrequency (%)
6.753018932 × 101138
0.3%
6.751520821 × 101129
0.2%
6.71044252 × 101129
0.2%
6.679939443 × 101138
0.3%
6.564759179 × 101145
0.4%
6.564729518 × 101137
0.3%
6.564626812 × 101138
0.3%
6.564589938 × 101141
0.3%
6.561271841 × 101138
0.3%
6.56116321 × 101145
0.4%

id
Text

UNIQUE 

Distinct11925
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size93.3 KiB
2024-07-25T12:27:57.414495image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length22
Median length22
Mean length21.948512
Min length20

Characters and Unicode

Total characters261736
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11925 ?
Unique (%)100.0%

Sample

1st rowCg4IqczJ9JMGEKDJ35KMEw
2nd rowCg4In-2Hk9MJEKDJ35KMEw
3rd rowCg4Iiriu-dMJEKDJ35KMEw
4th rowCg4I94XD6IgOEKDJ35KMEw
5th rowCg4I2paH64gOEKDJ35KMEw
ValueCountFrequency (%)
cg4iqczj9jmgekdj35kmew 1
 
< 0.1%
cg4iz9sxkaerekdj35kmew 1
 
< 0.1%
cg4il8y5kaerekdj35kmew 1
 
< 0.1%
cg4itue3kaerekdj35kmew 1
 
< 0.1%
cg4iiriu-dmjekdj35kmew 1
 
< 0.1%
cg4i94xd6igoekdj35kmew 1
 
< 0.1%
cg4i2pah64goekdj35kmew 1
 
< 0.1%
cg4izln-64goekdj35kmew 1
 
< 0.1%
cg4igmuvkaerekdj35kmew 1
 
< 0.1%
cg4ik8awkaerekdj35kmew 1
 
< 0.1%
Other values (11915) 11915
99.9%
2024-07-25T12:27:57.965117image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 19983
 
7.6%
g 18094
 
6.9%
I 17099
 
6.5%
C 15057
 
5.8%
4 14445
 
5.5%
Q 7524
 
2.9%
D 7255
 
2.8%
J 5778
 
2.2%
N 5620
 
2.1%
u 5440
 
2.1%
Other values (54) 145441
55.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 261736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 19983
 
7.6%
g 18094
 
6.9%
I 17099
 
6.5%
C 15057
 
5.8%
4 14445
 
5.5%
Q 7524
 
2.9%
D 7255
 
2.8%
J 5778
 
2.2%
N 5620
 
2.1%
u 5440
 
2.1%
Other values (54) 145441
55.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 261736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 19983
 
7.6%
g 18094
 
6.9%
I 17099
 
6.5%
C 15057
 
5.8%
4 14445
 
5.5%
Q 7524
 
2.9%
D 7255
 
2.8%
J 5778
 
2.2%
N 5620
 
2.1%
u 5440
 
2.1%
Other values (54) 145441
55.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 261736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 19983
 
7.6%
g 18094
 
6.9%
I 17099
 
6.5%
C 15057
 
5.8%
4 14445
 
5.5%
Q 7524
 
2.9%
D 7255
 
2.8%
J 5778
 
2.2%
N 5620
 
2.1%
u 5440
 
2.1%
Other values (54) 145441
55.6%

user_id
Real number (ℝ)

HIGH CORRELATION 

Distinct499
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0894451 × 1020
Minimum1.0000047 × 1020
Maximum1.1844148 × 1020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2024-07-25T12:27:58.303327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1.0000047 × 1020
5-th percentile1.0044257 × 1020
Q11.0433706 × 1020
median1.0885933 × 1020
Q31.134639 × 1020
95-th percentile1.1746229 × 1020
Maximum1.1844148 × 1020
Range1.8441017 × 1019
Interquartile range (IQR)9.1268435 × 1018

Descriptive statistics

Standard deviation5.3143386 × 1018
Coefficient of variation (CV)0.048780233
Kurtosis-1.1255544
Mean1.0894451 × 1020
Median Absolute Deviation (MAD)4.5762415 × 1018
Skewness-0.0060810444
Sum1.2991633 × 1024
Variance2.8242195 × 1037
MonotonicityNot monotonic
2024-07-25T12:27:58.628745image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.161004111 × 102087
 
0.7%
1.041908514 × 102082
 
0.7%
1.085048664 × 102081
 
0.7%
1.015578824 × 102081
 
0.7%
1.088573725 × 102081
 
0.7%
1.000004679 × 102080
 
0.7%
1.022008997 × 102076
 
0.6%
1.098295094 × 102075
 
0.6%
1.04979439 × 102071
 
0.6%
1.127310668 × 102069
 
0.6%
Other values (489) 11142
93.4%
ValueCountFrequency (%)
1.000004679 × 102080
0.7%
1.000189595 × 102031
 
0.3%
1.001445182 × 10201
 
< 0.1%
1.001599063 × 102039
0.3%
1.00170483 × 102028
 
0.2%
1.002410412 × 102014
 
0.1%
1.002649034 × 102062
0.5%
1.002854506 × 10203
 
< 0.1%
1.002889722 × 102019
 
0.2%
1.002995976 × 102053
0.4%
ValueCountFrequency (%)
1.18441485 × 102012
 
0.1%
1.184089549 × 102012
 
0.1%
1.183519473 × 102061
0.5%
1.183265698 × 10209
 
0.1%
1.183195696 × 102022
 
0.2%
1.183084509 × 102012
 
0.1%
1.183026838 × 102019
 
0.2%
1.182625808 × 102010
 
0.1%
1.182114896 × 102032
0.3%
1.181403569 × 102018
 
0.2%
Distinct11829
Distinct (%)99.8%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
Minimum2020-09-16 19:22:42.878000+00:00
Maximum2024-04-19 17:11:05.066000+00:00
2024-07-25T12:27:59.016309image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:59.281345image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct9709
Distinct (%)81.9%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
Minimum2020-09-16 19:22:50.062000+00:00
Maximum2024-04-28 00:23:46.833000+00:00
2024-07-25T12:27:59.496354image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:59.868478image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

late
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing8396
Missing (%)70.4%
Memory size93.3 KiB
1.0
3529 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10587
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3529
29.6%
(Missing) 8396
70.4%

Length

2024-07-25T12:28:00.063724image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T12:28:00.213444image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3529
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3529
33.3%
. 3529
33.3%
0 3529
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3529
33.3%
. 3529
33.3%
0 3529
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3529
33.3%
. 3529
33.3%
0 3529
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3529
33.3%
. 3529
33.3%
0 3529
33.3%
Distinct467
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size93.3 KiB
Minimum2020-09-16 19:22:09.603000
Maximum2024-04-19 17:06:31.930000
2024-07-25T12:28:00.384138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:28:00.589160image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct213
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size93.3 KiB
Minimum2020-09-24 00:00:00
Maximum2024-04-23 00:00:00
2024-07-25T12:28:00.775193image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:28:00.968254image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

available_time
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size93.3 KiB

grade
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct137
Distinct (%)1.8%
Missing4489
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean6.2872338
Minimum0
Maximum625
Zeros1297
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2024-07-25T12:28:01.148379image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7.2727273
Q310
95-th percentile10
Maximum625
Range625
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.1194053
Coefficient of variation (CV)1.2914114
Kurtosis4535.7611
Mean6.2872338
Median Absolute Deviation (MAD)2.7272727
Skewness59.468342
Sum46751.87
Variance65.924743
MonotonicityNot monotonic
2024-07-25T12:28:01.341850image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2324
19.5%
0 1297
 
10.9%
9 515
 
4.3%
8 450
 
3.8%
5 408
 
3.4%
7 343
 
2.9%
6 316
 
2.6%
4 224
 
1.9%
2 154
 
1.3%
3 153
 
1.3%
Other values (127) 1252
 
10.5%
(Missing) 4489
37.6%
ValueCountFrequency (%)
0 1297
10.9%
0.2 1
 
< 0.1%
0.25 2
 
< 0.1%
0.3 1
 
< 0.1%
0.35 1
 
< 0.1%
0.38 1
 
< 0.1%
0.4 2
 
< 0.1%
0.4761904762 1
 
< 0.1%
0.5 11
 
0.1%
0.6 5
 
< 0.1%
ValueCountFrequency (%)
625 1
 
< 0.1%
15 8
 
0.1%
14 4
 
< 0.1%
12 7
 
0.1%
11 5
 
< 0.1%
10 2324
19.5%
9.8 4
 
< 0.1%
9.6 1
 
< 0.1%
9.545454545 5
 
< 0.1%
9.523809524 3
 
< 0.1%
Distinct11829
Distinct (%)99.8%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
Minimum2020-09-16 19:22:42.878000
Maximum2024-04-19 17:11:05.066000
2024-07-25T12:28:01.546756image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:28:01.743719image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

first_turnin_time
Date

MISSING 

Distinct8373
Distinct (%)100.0%
Missing3552
Missing (%)29.8%
Memory size93.3 KiB
Minimum2020-09-16 20:23:13.127000
Maximum2024-04-24 12:41:21.433000
2024-07-25T12:28:01.936641image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:28:02.171741image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

first_reclaim_time
Date

MISSING 

Distinct8373
Distinct (%)100.0%
Missing3552
Missing (%)29.8%
Memory size93.3 KiB
Minimum2020-09-16 20:23:13.127000
Maximum2024-04-24 12:41:21.433000
2024-07-25T12:28:02.414088image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:28:02.659561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Unnamed: 14
Date

MISSING 

Distinct582
Distinct (%)100.0%
Missing11343
Missing (%)95.1%
Memory size93.3 KiB
Minimum2020-09-17 12:58:33.672000
Maximum2024-04-23 03:07:24.225000
2024-07-25T12:28:03.052834image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:28:03.318089image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1766
Distinct (%)14.9%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:03.647141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length9
Mean length8.7160577
Min length8

Characters and Unicode

Total characters103294
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1230 ?
Unique (%)10.4%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row0d0h3m11s
5th row0d0h1m35s
ValueCountFrequency (%)
0d0h0m0s 3492
29.5%
0d0h1m27s 54
 
0.5%
0d0h0m50s 49
 
0.4%
0d0h1m9s 49
 
0.4%
0d0h0m54s 48
 
0.4%
0d0h1m28s 48
 
0.4%
0d0h1m19s 47
 
0.4%
0d0h1m8s 47
 
0.4%
0d0h1m18s 46
 
0.4%
0d0h0m29s 46
 
0.4%
Other values (1756) 7925
66.9%
2024-07-25T12:28:04.194387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32841
31.8%
d 11851
 
11.5%
h 11851
 
11.5%
m 11851
 
11.5%
s 11851
 
11.5%
1 5566
 
5.4%
2 4285
 
4.1%
3 3335
 
3.2%
4 2793
 
2.7%
5 2602
 
2.5%
Other values (4) 4468
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103294
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32841
31.8%
d 11851
 
11.5%
h 11851
 
11.5%
m 11851
 
11.5%
s 11851
 
11.5%
1 5566
 
5.4%
2 4285
 
4.1%
3 3335
 
3.2%
4 2793
 
2.7%
5 2602
 
2.5%
Other values (4) 4468
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103294
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32841
31.8%
d 11851
 
11.5%
h 11851
 
11.5%
m 11851
 
11.5%
s 11851
 
11.5%
1 5566
 
5.4%
2 4285
 
4.1%
3 3335
 
3.2%
4 2793
 
2.7%
5 2602
 
2.5%
Other values (4) 4468
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103294
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32841
31.8%
d 11851
 
11.5%
h 11851
 
11.5%
m 11851
 
11.5%
s 11851
 
11.5%
1 5566
 
5.4%
2 4285
 
4.1%
3 3335
 
3.2%
4 2793
 
2.7%
5 2602
 
2.5%
Other values (4) 4468
 
4.3%
Distinct7917
Distinct (%)66.8%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:04.486344image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.47785
Min length8

Characters and Unicode

Total characters112322
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7526 ?
Unique (%)63.5%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row1d0h49m22s
5th row1d0h55m8s
ValueCountFrequency (%)
0d0h0m0s 3478
29.3%
0d0h46m53s 4
 
< 0.1%
0d0h19m58s 4
 
< 0.1%
0d0h54m0s 4
 
< 0.1%
0d1h27m12s 4
 
< 0.1%
0d0h43m40s 4
 
< 0.1%
0d1h29m10s 4
 
< 0.1%
0d0h42m7s 4
 
< 0.1%
0d1h10m37s 4
 
< 0.1%
0d0h44m44s 3
 
< 0.1%
Other values (7907) 8338
70.4%
2024-07-25T12:28:05.025036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21595
19.2%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9209
8.2%
2 7131
 
6.3%
3 5758
 
5.1%
5 5577
 
5.0%
4 5348
 
4.8%
Other values (4) 10300
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21595
19.2%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9209
8.2%
2 7131
 
6.3%
3 5758
 
5.1%
5 5577
 
5.0%
4 5348
 
4.8%
Other values (4) 10300
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21595
19.2%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9209
8.2%
2 7131
 
6.3%
3 5758
 
5.1%
5 5577
 
5.0%
4 5348
 
4.8%
Other values (4) 10300
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21595
19.2%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9209
8.2%
2 7131
 
6.3%
3 5758
 
5.1%
5 5577
 
5.0%
4 5348
 
4.8%
Other values (4) 10300
9.2%
Distinct7944
Distinct (%)67.0%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:05.303953image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.4972576
Min length8

Characters and Unicode

Total characters112552
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7573 ?
Unique (%)63.9%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row1d0h49m22s
5th row1d0h55m8s
ValueCountFrequency (%)
0d0h0m0s 3478
29.3%
0d1h27m12s 4
 
< 0.1%
0d0h46m53s 4
 
< 0.1%
0d0h19m58s 4
 
< 0.1%
0d1h10m37s 4
 
< 0.1%
0d1h29m10s 4
 
< 0.1%
0d0h30m7s 4
 
< 0.1%
0d0h42m7s 4
 
< 0.1%
0d0h22m2s 3
 
< 0.1%
0d0h49m31s 3
 
< 0.1%
Other values (7934) 8339
70.4%
2024-07-25T12:28:05.714952image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21432
19.0%
d 11851
10.5%
h 11851
10.5%
m 11851
10.5%
s 11851
10.5%
1 9239
8.2%
2 7253
 
6.4%
3 5807
 
5.2%
5 5582
 
5.0%
4 5372
 
4.8%
Other values (4) 10463
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21432
19.0%
d 11851
10.5%
h 11851
10.5%
m 11851
10.5%
s 11851
10.5%
1 9239
8.2%
2 7253
 
6.4%
3 5807
 
5.2%
5 5582
 
5.0%
4 5372
 
4.8%
Other values (4) 10463
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21432
19.0%
d 11851
10.5%
h 11851
10.5%
m 11851
10.5%
s 11851
10.5%
1 9239
8.2%
2 7253
 
6.4%
3 5807
 
5.2%
5 5582
 
5.0%
4 5372
 
4.8%
Other values (4) 10463
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21432
19.0%
d 11851
10.5%
h 11851
10.5%
m 11851
10.5%
s 11851
10.5%
1 9239
8.2%
2 7253
 
6.4%
3 5807
 
5.2%
5 5582
 
5.0%
4 5372
 
4.8%
Other values (4) 10463
9.3%
Distinct7886
Distinct (%)66.5%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:05.973065image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.4615644
Min length8

Characters and Unicode

Total characters112129
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7465 ?
Unique (%)63.0%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row1d0h46m10s
5th row1d0h53m33s
ValueCountFrequency (%)
0d0h0m0s 3478
29.3%
0d1h6m58s 4
 
< 0.1%
0d1h3m44s 4
 
< 0.1%
0d1h29m25s 4
 
< 0.1%
0d1h15m29s 4
 
< 0.1%
0d0h34m8s 4
 
< 0.1%
0d1h16m58s 4
 
< 0.1%
0d0h53m9s 4
 
< 0.1%
0d0h19m38s 4
 
< 0.1%
0d0h43m59s 4
 
< 0.1%
Other values (7876) 8337
70.3%
2024-07-25T12:28:06.393321image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21761
19.4%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9032
8.1%
2 7122
 
6.4%
3 5723
 
5.1%
5 5505
 
4.9%
4 5265
 
4.7%
Other values (4) 10317
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21761
19.4%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9032
8.1%
2 7122
 
6.4%
3 5723
 
5.1%
5 5505
 
4.9%
4 5265
 
4.7%
Other values (4) 10317
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21761
19.4%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9032
8.1%
2 7122
 
6.4%
3 5723
 
5.1%
5 5505
 
4.9%
4 5265
 
4.7%
Other values (4) 10317
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21761
19.4%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9032
8.1%
2 7122
 
6.4%
3 5723
 
5.1%
5 5505
 
4.9%
4 5265
 
4.7%
Other values (4) 10317
9.2%
Distinct7910
Distinct (%)66.7%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:06.646442image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.4797064
Min length8

Characters and Unicode

Total characters112344
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7505 ?
Unique (%)63.3%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row1d0h46m10s
5th row1d0h53m33s
ValueCountFrequency (%)
0d0h0m0s 3478
29.3%
0d0h43m59s 4
 
< 0.1%
0d0h53m9s 4
 
< 0.1%
0d1h15m29s 4
 
< 0.1%
0d0h9m10s 4
 
< 0.1%
0d0h34m8s 4
 
< 0.1%
0d1h3m44s 4
 
< 0.1%
0d1h6m58s 4
 
< 0.1%
0d0h32m44s 3
 
< 0.1%
0d1h16m59s 3
 
< 0.1%
Other values (7900) 8339
70.4%
2024-07-25T12:28:07.270072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21604
19.2%
d 11851
10.5%
h 11851
10.5%
m 11851
10.5%
s 11851
10.5%
1 9103
8.1%
2 7221
 
6.4%
3 5794
 
5.2%
5 5467
 
4.9%
4 5261
 
4.7%
Other values (4) 10490
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21604
19.2%
d 11851
10.5%
h 11851
10.5%
m 11851
10.5%
s 11851
10.5%
1 9103
8.1%
2 7221
 
6.4%
3 5794
 
5.2%
5 5467
 
4.9%
4 5261
 
4.7%
Other values (4) 10490
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21604
19.2%
d 11851
10.5%
h 11851
10.5%
m 11851
10.5%
s 11851
10.5%
1 9103
8.1%
2 7221
 
6.4%
3 5794
 
5.2%
5 5467
 
4.9%
4 5261
 
4.7%
Other values (4) 10490
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21604
19.2%
d 11851
10.5%
h 11851
10.5%
m 11851
10.5%
s 11851
10.5%
1 9103
8.1%
2 7221
 
6.4%
3 5794
 
5.2%
5 5467
 
4.9%
4 5261
 
4.7%
Other values (4) 10490
9.3%
Distinct470
Distinct (%)4.0%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:07.705545image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.0720614
Min length8

Characters and Unicode

Total characters95662
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique411 ?
Unique (%)3.5%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row0d0h0m0s
5th row0d0h0m0s
ValueCountFrequency (%)
0d0h0m0s 11269
95.1%
0d0h0m7s 12
 
0.1%
0d0h0m8s 9
 
0.1%
0d0h0m5s 7
 
0.1%
0d0h0m50s 6
 
0.1%
0d0h0m4s 6
 
0.1%
0d0h0m9s 5
 
< 0.1%
0d0h0m6s 4
 
< 0.1%
0d0h0m12s 4
 
< 0.1%
0d0h0m11s 4
 
< 0.1%
Other values (460) 525
 
4.4%
2024-07-25T12:28:08.382659image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 46159
48.3%
d 11851
 
12.4%
h 11851
 
12.4%
m 11851
 
12.4%
s 11851
 
12.4%
1 426
 
0.4%
2 390
 
0.4%
3 311
 
0.3%
5 259
 
0.3%
4 247
 
0.3%
Other values (4) 466
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 46159
48.3%
d 11851
 
12.4%
h 11851
 
12.4%
m 11851
 
12.4%
s 11851
 
12.4%
1 426
 
0.4%
2 390
 
0.4%
3 311
 
0.3%
5 259
 
0.3%
4 247
 
0.3%
Other values (4) 466
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 46159
48.3%
d 11851
 
12.4%
h 11851
 
12.4%
m 11851
 
12.4%
s 11851
 
12.4%
1 426
 
0.4%
2 390
 
0.4%
3 311
 
0.3%
5 259
 
0.3%
4 247
 
0.3%
Other values (4) 466
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 46159
48.3%
d 11851
 
12.4%
h 11851
 
12.4%
m 11851
 
12.4%
s 11851
 
12.4%
1 426
 
0.4%
2 390
 
0.4%
3 311
 
0.3%
5 259
 
0.3%
4 247
 
0.3%
Other values (4) 466
 
0.5%
Distinct679
Distinct (%)5.7%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:08.687796image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.1226901
Min length8

Characters and Unicode

Total characters96262
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique630 ?
Unique (%)5.3%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row0d0h0m0s
5th row0d0h0m0s
ValueCountFrequency (%)
0d0h0m0s 11096
93.6%
0d0h0m23s 6
 
0.1%
0d0h0m11s 5
 
< 0.1%
0d0h0m7s 5
 
< 0.1%
0d0h0m19s 5
 
< 0.1%
0d0h0m13s 4
 
< 0.1%
0d0h0m24s 4
 
< 0.1%
0d0h0m16s 4
 
< 0.1%
0d0h0m1s 3
 
< 0.1%
0d0h1m16s 3
 
< 0.1%
Other values (669) 716
 
6.0%
2024-07-25T12:28:09.128737image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45525
47.3%
d 11851
 
12.3%
h 11851
 
12.3%
m 11851
 
12.3%
s 11851
 
12.3%
1 683
 
0.7%
2 631
 
0.7%
3 457
 
0.5%
4 389
 
0.4%
5 375
 
0.4%
Other values (4) 798
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 45525
47.3%
d 11851
 
12.3%
h 11851
 
12.3%
m 11851
 
12.3%
s 11851
 
12.3%
1 683
 
0.7%
2 631
 
0.7%
3 457
 
0.5%
4 389
 
0.4%
5 375
 
0.4%
Other values (4) 798
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 45525
47.3%
d 11851
 
12.3%
h 11851
 
12.3%
m 11851
 
12.3%
s 11851
 
12.3%
1 683
 
0.7%
2 631
 
0.7%
3 457
 
0.5%
4 389
 
0.4%
5 375
 
0.4%
Other values (4) 798
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 45525
47.3%
d 11851
 
12.3%
h 11851
 
12.3%
m 11851
 
12.3%
s 11851
 
12.3%
1 683
 
0.7%
2 631
 
0.7%
3 457
 
0.5%
4 389
 
0.4%
5 375
 
0.4%
Other values (4) 798
 
0.8%
Distinct435
Distinct (%)74.7%
Missing11343
Missing (%)95.1%
Memory size93.3 KiB
2024-07-25T12:28:09.436286image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length9
Mean length9.4656357
Min length8

Characters and Unicode

Total characters5509
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique356 ?
Unique (%)61.2%

Sample

1st row0d1h1m26s
2nd row0d0h4m30s
3rd row0d0h0m30s
4th row0d0h18m16s
5th row8d0h14m52s
ValueCountFrequency (%)
0d0h0m27s 7
 
1.2%
0d0h0m56s 6
 
1.0%
0d0h1m0s 5
 
0.9%
0d0h0m24s 5
 
0.9%
0d0h0m31s 5
 
0.9%
0d0h0m14s 5
 
0.9%
0d0h0m36s 5
 
0.9%
0d0h0m39s 5
 
0.9%
0d0h0m30s 5
 
0.9%
0d0h1m21s 4
 
0.7%
Other values (425) 530
91.1%
2024-07-25T12:28:10.199877image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1184
21.5%
d 582
10.6%
h 582
10.6%
m 582
10.6%
s 582
10.6%
1 415
 
7.5%
2 374
 
6.8%
3 294
 
5.3%
5 265
 
4.8%
4 221
 
4.0%
Other values (5) 428
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1184
21.5%
d 582
10.6%
h 582
10.6%
m 582
10.6%
s 582
10.6%
1 415
 
7.5%
2 374
 
6.8%
3 294
 
5.3%
5 265
 
4.8%
4 221
 
4.0%
Other values (5) 428
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1184
21.5%
d 582
10.6%
h 582
10.6%
m 582
10.6%
s 582
10.6%
1 415
 
7.5%
2 374
 
6.8%
3 294
 
5.3%
5 265
 
4.8%
4 221
 
4.0%
Other values (5) 428
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1184
21.5%
d 582
10.6%
h 582
10.6%
m 582
10.6%
s 582
10.6%
1 415
 
7.5%
2 374
 
6.8%
3 294
 
5.3%
5 265
 
4.8%
4 221
 
4.0%
Other values (5) 428
 
7.8%

effective_reclaim_duration
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct548
Distinct (%)4.6%
Missing74
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.026871521
Minimum-0.0091186574
Maximum68.186525
Zeros11304
Zeros (%)94.8%
Negative7
Negative (%)0.1%
Memory size93.3 KiB
2024-07-25T12:28:10.678377image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0091186574
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum68.186525
Range68.195644
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80505514
Coefficient of variation (CV)29.959418
Kurtosis4642.6767
Mean0.026871521
Median Absolute Deviation (MAD)0
Skewness61.32975
Sum318.4544
Variance0.64811378
MonotonicityNot monotonic
2024-07-25T12:28:11.283318image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11304
94.8%
0.05753131944 1
 
< 0.1%
0.005141828704 1
 
< 0.1%
0.005090104167 1
 
< 0.1%
0.003187916667 1
 
< 0.1%
0.002380844907 1
 
< 0.1%
0.000835462963 1
 
< 0.1%
0.0004547685185 1
 
< 0.1%
0.007113761574 1
 
< 0.1%
0.01253177083 1
 
< 0.1%
Other values (538) 538
 
4.5%
(Missing) 74
 
0.6%
ValueCountFrequency (%)
-0.009118657407 1
 
< 0.1%
-0.0002712962963 1
 
< 0.1%
-8.939814815 × 10-51
 
< 0.1%
-7.762731481 × 10-51
 
< 0.1%
-6.744212963 × 10-51
 
< 0.1%
-3.965277778 × 10-51
 
< 0.1%
-3.766203704 × 10-51
 
< 0.1%
0 11304
94.8%
3.200231481 × 10-51
 
< 0.1%
3.300925926 × 10-51
 
< 0.1%
ValueCountFrequency (%)
68.1865251 1
< 0.1%
26.8924974 1
< 0.1%
26.85238653 1
< 0.1%
25.52228155 1
< 0.1%
14.01899019 1
< 0.1%
9.065702454 1
< 0.1%
8.005139294 1
< 0.1%
6.983381088 1
< 0.1%
6.981680775 1
< 0.1%
6.964957905 1
< 0.1%

mean_reclaim_duration
Text

MISSING 

Distinct426
Distinct (%)73.2%
Missing11343
Missing (%)95.1%
Memory size93.3 KiB
2024-07-25T12:28:11.780352image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length9
Mean length9.4295533
Min length8

Characters and Unicode

Total characters5488
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)59.5%

Sample

1st row0d1h1m26s
2nd row0d0h4m30s
3rd row0d0h0m30s
4th row0d0h18m16s
5th row4d0h7m26s
ValueCountFrequency (%)
0d0h0m27s 8
 
1.4%
0d0h0m56s 7
 
1.2%
0d0h1m0s 5
 
0.9%
0d0h0m24s 5
 
0.9%
0d0h0m31s 5
 
0.9%
0d0h0m36s 5
 
0.9%
0d0h0m20s 5
 
0.9%
0d0h0m14s 5
 
0.9%
0d0h0m39s 5
 
0.9%
0d0h0m6s 5
 
0.9%
Other values (416) 527
90.5%
2024-07-25T12:28:13.235771image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1196
21.8%
d 582
10.6%
h 582
10.6%
m 582
10.6%
s 582
10.6%
1 413
 
7.5%
2 348
 
6.3%
3 277
 
5.0%
5 255
 
4.6%
4 229
 
4.2%
Other values (5) 442
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1196
21.8%
d 582
10.6%
h 582
10.6%
m 582
10.6%
s 582
10.6%
1 413
 
7.5%
2 348
 
6.3%
3 277
 
5.0%
5 255
 
4.6%
4 229
 
4.2%
Other values (5) 442
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1196
21.8%
d 582
10.6%
h 582
10.6%
m 582
10.6%
s 582
10.6%
1 413
 
7.5%
2 348
 
6.3%
3 277
 
5.0%
5 255
 
4.6%
4 229
 
4.2%
Other values (5) 442
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1196
21.8%
d 582
10.6%
h 582
10.6%
m 582
10.6%
s 582
10.6%
1 413
 
7.5%
2 348
 
6.3%
3 277
 
5.0%
5 255
 
4.6%
4 229
 
4.2%
Other values (5) 442
 
8.1%
Distinct365
Distinct (%)62.7%
Missing11343
Missing (%)95.1%
Memory size93.3 KiB
2024-07-25T12:28:13.674230image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.1219931
Min length8

Characters and Unicode

Total characters5309
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique275 ?
Unique (%)47.3%

Sample

1st row0d1h1m26s
2nd row0d0h4m30s
3rd row0d0h0m30s
4th row0d0h18m16s
5th row4d0h3m42s
ValueCountFrequency (%)
0d0h0m0s 35
 
6.0%
0d0h0m27s 9
 
1.5%
0d0h0m56s 8
 
1.4%
0d0h0m36s 6
 
1.0%
0d0h0m31s 6
 
1.0%
0d0h0m17s 5
 
0.9%
0d0h0m21s 5
 
0.9%
0d0h0m20s 5
 
0.9%
0d0h0m22s 5
 
0.9%
0d0h0m30s 5
 
0.9%
Other values (355) 493
84.7%
2024-07-25T12:28:14.753004image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1382
26.0%
d 582
11.0%
h 582
11.0%
m 582
11.0%
s 582
11.0%
1 333
 
6.3%
2 306
 
5.8%
3 224
 
4.2%
5 206
 
3.9%
4 202
 
3.8%
Other values (5) 328
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1382
26.0%
d 582
11.0%
h 582
11.0%
m 582
11.0%
s 582
11.0%
1 333
 
6.3%
2 306
 
5.8%
3 224
 
4.2%
5 206
 
3.9%
4 202
 
3.8%
Other values (5) 328
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1382
26.0%
d 582
11.0%
h 582
11.0%
m 582
11.0%
s 582
11.0%
1 333
 
6.3%
2 306
 
5.8%
3 224
 
4.2%
5 206
 
3.9%
4 202
 
3.8%
Other values (5) 328
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1382
26.0%
d 582
11.0%
h 582
11.0%
m 582
11.0%
s 582
11.0%
1 333
 
6.3%
2 306
 
5.8%
3 224
 
4.2%
5 206
 
3.9%
4 202
 
3.8%
Other values (5) 328
 
6.2%
Distinct94
Distinct (%)16.2%
Missing11343
Missing (%)95.1%
Memory size93.3 KiB
2024-07-25T12:28:15.477277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.2353952
Min length8

Characters and Unicode

Total characters4793
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)15.1%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row0d0h0m0s
5th row0d0h3m44s
ValueCountFrequency (%)
0d0h0m0s 484
83.2%
0d0h0m55s 2
 
0.3%
0d0h0m9s 2
 
0.3%
0d0h0m14s 2
 
0.3%
0d0h0m10s 2
 
0.3%
0d0h2m46s 2
 
0.3%
0d1h28m59s 1
 
0.2%
0d0h13m45s 1
 
0.2%
0d0h46m43s 1
 
0.2%
0d0h11m57s 1
 
0.2%
Other values (84) 84
 
14.4%
2024-07-25T12:28:16.386259image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2130
44.4%
d 582
 
12.1%
h 582
 
12.1%
m 582
 
12.1%
s 582
 
12.1%
1 68
 
1.4%
4 52
 
1.1%
5 50
 
1.0%
3 41
 
0.9%
2 39
 
0.8%
Other values (4) 85
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2130
44.4%
d 582
 
12.1%
h 582
 
12.1%
m 582
 
12.1%
s 582
 
12.1%
1 68
 
1.4%
4 52
 
1.1%
5 50
 
1.0%
3 41
 
0.9%
2 39
 
0.8%
Other values (4) 85
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2130
44.4%
d 582
 
12.1%
h 582
 
12.1%
m 582
 
12.1%
s 582
 
12.1%
1 68
 
1.4%
4 52
 
1.1%
5 50
 
1.0%
3 41
 
0.9%
2 39
 
0.8%
Other values (4) 85
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2130
44.4%
d 582
 
12.1%
h 582
 
12.1%
m 582
 
12.1%
s 582
 
12.1%
1 68
 
1.4%
4 52
 
1.1%
5 50
 
1.0%
3 41
 
0.9%
2 39
 
0.8%
Other values (4) 85
 
1.8%
Distinct7878
Distinct (%)66.5%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:16.783000image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.4641802
Min length8

Characters and Unicode

Total characters112160
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7449 ?
Unique (%)62.9%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row1d0h46m10s
5th row1d0h53m33s
ValueCountFrequency (%)
0d0h0m0s 3478
29.3%
0d0h54m48s 4
 
< 0.1%
0d0h34m8s 4
 
< 0.1%
0d0h53m9s 4
 
< 0.1%
0d0h43m59s 4
 
< 0.1%
0d1h0m55s 4
 
< 0.1%
0d1h3m44s 4
 
< 0.1%
0d1h15m29s 4
 
< 0.1%
0d0h14m51s 3
 
< 0.1%
0d0h52m14s 3
 
< 0.1%
Other values (7868) 8339
70.4%
2024-07-25T12:28:17.364919image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21717
19.4%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9049
8.1%
2 7131
 
6.4%
3 5738
 
5.1%
5 5505
 
4.9%
4 5281
 
4.7%
Other values (4) 10335
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21717
19.4%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9049
8.1%
2 7131
 
6.4%
3 5738
 
5.1%
5 5505
 
4.9%
4 5281
 
4.7%
Other values (4) 10335
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21717
19.4%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9049
8.1%
2 7131
 
6.4%
3 5738
 
5.1%
5 5505
 
4.9%
4 5281
 
4.7%
Other values (4) 10335
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21717
19.4%
d 11851
10.6%
h 11851
10.6%
m 11851
10.6%
s 11851
10.6%
1 9049
8.1%
2 7131
 
6.4%
3 5738
 
5.1%
5 5505
 
4.9%
4 5281
 
4.7%
Other values (4) 10335
9.2%

idle_reclaim_duration
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
0d0h0m0s
11851 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters94808
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row0d0h0m0s
5th row0d0h0m0s

Common Values

ValueCountFrequency (%)
0d0h0m0s 11851
99.4%
(Missing) 74
 
0.6%

Length

2024-07-25T12:28:17.629407image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T12:28:17.902546image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0d0h0m0s 11851
100.0%

Most occurring characters

ValueCountFrequency (%)
0 47404
50.0%
d 11851
 
12.5%
h 11851
 
12.5%
m 11851
 
12.5%
s 11851
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 47404
50.0%
d 11851
 
12.5%
h 11851
 
12.5%
m 11851
 
12.5%
s 11851
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 47404
50.0%
d 11851
 
12.5%
h 11851
 
12.5%
m 11851
 
12.5%
s 11851
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 47404
50.0%
d 11851
 
12.5%
h 11851
 
12.5%
m 11851
 
12.5%
s 11851
 
12.5%
Distinct2114
Distinct (%)17.8%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:18.492945image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.7586702
Min length8

Characters and Unicode

Total characters103799
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1549 ?
Unique (%)13.1%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row0d0h3m11s
5th row0d0h1m35s
ValueCountFrequency (%)
0d0h0m0s 3491
29.5%
0d0h1m27s 50
 
0.4%
0d0h1m9s 48
 
0.4%
0d0h1m18s 46
 
0.4%
0d0h1m8s 46
 
0.4%
0d0h1m1s 45
 
0.4%
0d0h0m54s 45
 
0.4%
0d0h1m19s 44
 
0.4%
0d0h0m58s 43
 
0.4%
0d0h0m50s 43
 
0.4%
Other values (2104) 7950
67.1%
2024-07-25T12:28:19.570650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32342
31.2%
d 11851
 
11.4%
h 11851
 
11.4%
m 11851
 
11.4%
s 11851
 
11.4%
1 5787
 
5.6%
2 4416
 
4.3%
3 3466
 
3.3%
4 2911
 
2.8%
5 2772
 
2.7%
Other values (4) 4701
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32342
31.2%
d 11851
 
11.4%
h 11851
 
11.4%
m 11851
 
11.4%
s 11851
 
11.4%
1 5787
 
5.6%
2 4416
 
4.3%
3 3466
 
3.3%
4 2911
 
2.8%
5 2772
 
2.7%
Other values (4) 4701
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32342
31.2%
d 11851
 
11.4%
h 11851
 
11.4%
m 11851
 
11.4%
s 11851
 
11.4%
1 5787
 
5.6%
2 4416
 
4.3%
3 3466
 
3.3%
4 2911
 
2.8%
5 2772
 
2.7%
Other values (4) 4701
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32342
31.2%
d 11851
 
11.4%
h 11851
 
11.4%
m 11851
 
11.4%
s 11851
 
11.4%
1 5787
 
5.6%
2 4416
 
4.3%
3 3466
 
3.3%
4 2911
 
2.8%
5 2772
 
2.7%
Other values (4) 4701
 
4.5%

time_usage_ratio
Real number (ℝ)

SKEWED  ZEROS 

Distinct8324
Distinct (%)70.2%
Missing74
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean72.698382
Minimum-61689.9
Maximum176952.71
Zeros3528
Zeros (%)29.6%
Negative1937
Negative (%)16.2%
Memory size93.3 KiB
2024-07-25T12:28:19.947321image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-61689.9
5-th percentile-10.402554
Q10
median2.3389936
Q387.246181
95-th percentile181.17719
Maximum176952.71
Range238642.6
Interquartile range (IQR)87.246181

Descriptive statistics

Standard deviation2342.6585
Coefficient of variation (CV)32.224355
Kurtosis2998.9453
Mean72.698382
Median Absolute Deviation (MAD)11.077168
Skewness40.553109
Sum861548.53
Variance5488048.6
MonotonicityNot monotonic
2024-07-25T12:28:20.444584image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3528
29.6%
30.05519901 1
 
< 0.1%
98.93791731 1
 
< 0.1%
80.1835505 1
 
< 0.1%
196.7041685 1
 
< 0.1%
120.187708 1
 
< 0.1%
93.57068257 1
 
< 0.1%
92.59432939 1
 
< 0.1%
132.8131089 1
 
< 0.1%
95.84009059 1
 
< 0.1%
Other values (8314) 8314
69.7%
(Missing) 74
 
0.6%
ValueCountFrequency (%)
-61689.89973 1
< 0.1%
-38236.64749 1
< 0.1%
-27046.73553 1
< 0.1%
-23806.49581 1
< 0.1%
-21563.03762 1
< 0.1%
-19166.42896 1
< 0.1%
-19013.19709 1
< 0.1%
-19011.62084 1
< 0.1%
-18950.68968 1
< 0.1%
-18950.67392 1
< 0.1%
ValueCountFrequency (%)
176952.7052 1
< 0.1%
81501.92175 1
< 0.1%
68534.55219 1
< 0.1%
46184.07427 1
< 0.1%
25202.71388 1
< 0.1%
23992.9907 1
< 0.1%
20542.18369 1
< 0.1%
20476.08209 1
< 0.1%
20473.96532 1
< 0.1%
20473.77044 1
< 0.1%

turned_in
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
1.0
8942 
0.0
2909 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35553
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8942
75.0%
0.0 2909
 
24.4%
(Missing) 74
 
0.6%

Length

2024-07-25T12:28:20.947886image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T12:28:21.335687image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8942
75.5%
0.0 2909
 
24.5%

Most occurring characters

ValueCountFrequency (%)
0 14760
41.5%
. 11851
33.3%
1 8942
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14760
41.5%
. 11851
33.3%
1 8942
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14760
41.5%
. 11851
33.3%
1 8942
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14760
41.5%
. 11851
33.3%
1 8942
25.2%

turnin_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing74
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.78271876
Minimum0
Maximum9
Zeros3478
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2024-07-25T12:28:21.618039image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.59587193
Coefficient of variation (CV)0.76128485
Kurtosis5.1899507
Mean0.78271876
Median Absolute Deviation (MAD)0
Skewness0.77360478
Sum9276
Variance0.35506336
MonotonicityNot monotonic
2024-07-25T12:28:21.862875image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 7618
63.9%
0 3478
29.2%
2 635
 
5.3%
3 99
 
0.8%
4 18
 
0.2%
5 2
 
< 0.1%
9 1
 
< 0.1%
(Missing) 74
 
0.6%
ValueCountFrequency (%)
0 3478
29.2%
1 7618
63.9%
2 635
 
5.3%
3 99
 
0.8%
4 18
 
0.2%
5 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
5 2
 
< 0.1%
4 18
 
0.2%
3 99
 
0.8%
2 635
 
5.3%
1 7618
63.9%
0 3478
29.2%

reclaim_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing74
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.060416842
Minimum0
Maximum8
Zeros11269
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size93.3 KiB
2024-07-25T12:28:22.094441image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29435498
Coefficient of variation (CV)4.8720683
Kurtosis78.865348
Mean0.060416842
Median Absolute Deviation (MAD)0
Skewness6.8479712
Sum716
Variance0.086644856
MonotonicityNot monotonic
2024-07-25T12:28:22.321387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11269
94.5%
1 474
 
4.0%
2 89
 
0.7%
3 16
 
0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
(Missing) 74
 
0.6%
ValueCountFrequency (%)
0 11269
94.5%
1 474
 
4.0%
2 89
 
0.7%
3 16
 
0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
4 2
 
< 0.1%
3 16
 
0.1%
2 89
 
0.7%
1 474
 
4.0%
0 11269
94.5%
Distinct8192
Distinct (%)69.1%
Missing74
Missing (%)0.6%
Memory size93.3 KiB
2024-07-25T12:28:22.848511image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length14
Median length13
Mean length9.8612775
Min length8

Characters and Unicode

Total characters116866
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8017 ?
Unique (%)67.6%

Sample

1st row0d0h0m0s
2nd row0d0h0m0s
3rd row0d0h0m0s
4th row5d6h47m53s
5th row5d6h42m6s
ValueCountFrequency (%)
0d0h0m0s 3478
29.3%
1d10h28m49s 3
 
< 0.1%
0d12h20m48s 3
 
< 0.1%
1d9h11m31s 3
 
< 0.1%
1d10h19m31s 3
 
< 0.1%
1d13h7m23s 3
 
< 0.1%
1d10h19m36s 3
 
< 0.1%
1d12h24m6s 3
 
< 0.1%
1d10h24m29s 3
 
< 0.1%
1d4h59m7s 2
 
< 0.1%
Other values (8156) 8347
70.4%
2024-07-25T12:28:24.200129image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19289
16.5%
d 11851
10.1%
h 11851
10.1%
m 11851
10.1%
s 11851
10.1%
1 11275
9.6%
2 7900
6.8%
3 6038
 
5.2%
5 5503
 
4.7%
4 5461
 
4.7%
Other values (5) 13996
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19289
16.5%
d 11851
10.1%
h 11851
10.1%
m 11851
10.1%
s 11851
10.1%
1 11275
9.6%
2 7900
6.8%
3 6038
 
5.2%
5 5503
 
4.7%
4 5461
 
4.7%
Other values (5) 13996
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19289
16.5%
d 11851
10.1%
h 11851
10.1%
m 11851
10.1%
s 11851
10.1%
1 11275
9.6%
2 7900
6.8%
3 6038
 
5.2%
5 5503
 
4.7%
4 5461
 
4.7%
Other values (5) 13996
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19289
16.5%
d 11851
10.1%
h 11851
10.1%
m 11851
10.1%
s 11851
10.1%
1 11275
9.6%
2 7900
6.8%
3 6038
 
5.2%
5 5503
 
4.7%
4 5461
 
4.7%
Other values (5) 13996
12.0%

Interactions

2024-07-25T12:27:51.461108image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:31.516015image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:33.688016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:35.814939image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:43.385952image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:45.942526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:48.113152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:49.717718image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:51.590994image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:31.666914image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:33.840478image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:36.515609image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:43.519753image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:46.129486image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:48.236209image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:49.827713image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:51.729998image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:31.816260image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:34.036012image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:37.402154image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:43.659716image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:46.349858image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:48.369797image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:50.128091image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:52.578957image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:32.902082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:35.048650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:39.267227image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:44.620295image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:47.398134image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:49.107949image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:50.816924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:52.712001image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:33.072045image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:35.190829image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:40.253821image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:44.833157image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:47.543816image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:49.242325image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:50.954792image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:52.852959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:33.243870image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:35.330819image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:40.995425image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:45.185888image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:47.692474image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:49.364719image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:51.094928image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:52.990802image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:33.384834image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:35.457624image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:41.839939image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:45.479704image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:47.828001image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:49.475681image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:51.213652image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:53.139841image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:33.513057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:35.655815image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:42.637466image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:45.701138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:47.960038image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:49.582716image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T12:27:51.323997image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-25T12:28:24.524793image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
course_ideffective_reclaim_durationgradereclaim_counttask_idtime_usage_ratioturned_inturnin_countuser_id
course_id1.0000.1010.0930.1000.913-0.1620.2160.102-0.019
effective_reclaim_duration0.1011.0000.0290.9440.1220.0360.0220.414-0.011
grade0.0930.0291.0000.0110.0580.1920.0000.466-0.032
reclaim_count0.1000.9440.0111.0000.1210.0380.1070.419-0.010
task_id0.9130.1220.0580.1211.000-0.1930.1600.088-0.024
time_usage_ratio-0.1620.0360.1920.038-0.1931.0000.0260.3950.035
turned_in0.2160.0220.0000.1070.1600.0261.0000.8750.563
turnin_count0.1020.4140.4660.4190.0880.3950.8751.0000.008
user_id-0.019-0.011-0.032-0.010-0.0240.0350.5630.0081.000

Missing values

2024-07-25T12:27:53.418694image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-25T12:27:54.125064image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-25T12:27:55.403237image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

course_idtask_ididuser_idcreation_timeupdate_timelatetask_creation_timedue_datetimeavailable_timegradefirst_view_timefirst_turnin_timefirst_reclaim_timeUnnamed: 14task_creation_to_first_view_durationtask_creation_to_first_turnin_durationtask_creation_to_last_turnin_durationfirst_view_to_first_turnin_durationfirst_view_to_last_turnin_durationfirst_turnin_to_first_reclaim_durationfirst_turnin_to_last_turnin_durationfirst_reclaim_to_last_turnin_durationeffective_reclaim_durationmean_reclaim_durationmean_effective_reclaim_durationmean_time_between_reclaimstotal_doing_durationidle_reclaim_durationtotal_idle_durationtime_usage_ratioturned_inturnin_countreclaim_countlast_turnin_to_due_time
0670695200974656095569056Cg4IqczJ9JMGEKDJ35KMEw1127310667576077476832024-03-29T12:04:39.672Z2024-03-29T12:04:39.640Z1.02024-03-21 16:22:44.3492024-03-286d7h37m15sNaN2024-03-29 12:04:39.672NaTNaTNaT0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0sNaN0.0NaNNaNNaN0d0h0m0s0d0h0m0s0d0h0m0s0.0000000.00.00.00d0h0m0s
1670695200974656095569056Cg4In-2Hk9MJEKDJ35KMEw1134732193817234009932024-04-11T02:25:35.247Z2024-04-11T02:25:35.111Z1.02024-03-21 16:22:44.3492024-03-286d7h37m15sNaN2024-04-11 02:25:35.247NaTNaTNaT0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0sNaN0.0NaNNaNNaN0d0h0m0s0d0h0m0s0d0h0m0s0.0000000.00.00.00d0h0m0s
2670695200974656095569056Cg4Iiriu-dMJEKDJ35KMEw1182114896143900062602024-03-29T10:44:37.122Z2024-03-29T10:44:36.999Z1.02024-03-21 16:22:44.3492024-03-286d7h37m15sNaN2024-03-29 10:44:37.122NaTNaTNaT0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0s0d0h0m0sNaN0.0NaNNaNNaN0d0h0m0s0d0h0m0s0d0h0m0s0.0000000.00.00.00d0h0m0s
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